Hostname: page-component-848d4c4894-xfwgj Total loading time: 0 Render date: 2024-06-26T01:35:55.387Z Has data issue: false hasContentIssue false
  • English
  • Français

Incremental Answer Set Programming with Overgrounding

Published online by Cambridge University Press:  20 September 2019

FRANCESCO CALIMERI Open the ORCID record for FRANCESCO CALIMERI [Opens in a new window] ,
GIOVAMBATTISTA IANNI ,
FRANCESCO PACENZA Open the ORCID record for FRANCESCO PACENZA [Opens in a new window] ,
SIMONA PERRI Open the ORCID record for SIMONA PERRI [Opens in a new window]  and
JESSICA ZANGARI Open the ORCID record for JESSICA ZANGARI [Opens in a new window]
FRANCESCO CALIMERI
Affiliation:
Department of Mathematics and Computer Science, University of Calabria, Italy (e-mail: lastname@mat.unical.it)-https://www.mat.unical.it
GIOVAMBATTISTA IANNI
Affiliation:
Department of Mathematics and Computer Science, University of Calabria, Italy (e-mail: lastname@mat.unical.it)-https://www.mat.unical.it
FRANCESCO PACENZA
Affiliation:
Department of Mathematics and Computer Science, University of Calabria, Italy (e-mail: lastname@mat.unical.it)-https://www.mat.unical.it
SIMONA PERRI
Affiliation:
Department of Mathematics and Computer Science, University of Calabria, Italy (e-mail: lastname@mat.unical.it)-https://www.mat.unical.it
JESSICA ZANGARI
Affiliation:
Department of Mathematics and Computer Science, University of Calabria, Italy (e-mail: lastname@mat.unical.it)-https://www.mat.unical.it
Get access Rights & Permissions [Opens in a new window]

Abstract

Repeated executions of reasoning tasks for varying inputs are necessary in many applicative settings, such as stream reasoning. In this context, we propose an incremental grounding approach for the answer set semantics. We focus on the possibility of generating incrementally larger ground logic programs equivalent to a given non-ground one; so called overgrounded programs can be reused in combination with deliberately many different sets of inputs. Updating overgrounded programs requires a small effort, thus making the instantiation of logic programs considerably faster when grounding is repeated on a series of inputs similar to each other. Notably, the proposed approach works “under the hood”, relieving designers of logic programs from controlling technical aspects of grounding engines and answer set systems. In this work we present the theoretical basis of the proposed incremental grounding technique, we illustrate the consequent repeated evaluation strategy and report about our experiments.

Keywords

Knowledge RepresentationAnswer Set ProgrammingStream ReasoningGroundingInstantiation of Logic ProgramsOvergrounding
Type
Original Article
Information
Theory and Practice of Logic Programming , Volume 19 , Special Issue 5-6: 35th International Conference on Logic Programming , September 2019 , pp. 957 - 973
Copyright
© Cambridge University Press 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Alviano, M., Dodaro, C., Leone, N., and Ricca, F. 2015. Advances in WASP. In Calimeri, F., Ianni, G., and Truszczynski, M. (Eds.), LPNMR 2015, Volume 9345 of LNCS, pp. 40–54. Springer.Google Scholar
Alviano, M., Faber, W., Greco, G., and Leone, N. 2012. Magic sets for disjunctive datalog programs. Artificial Intelligence 187, 156–192.Google Scholar
Beck, H., Eiter, T., and Folie, C. 2017. Ticker: A system for incremental asp-based stream reasoning. TPLP 17, 5-6, 744–763.Google Scholar
Bogaerts, B. and Weinzierl, A. 2018. Exploiting justifications for lazy grounding of answer set programs. In IJCAI 2018, July 13-19, 2018, Stockholm, Sweden., pp. 1737–1745. ijcai.org.Google Scholar
Calimeri, F., Cozza, S., Ianni, G., and Leone, N. 2008. Computable functions in ASP: theory and implementation. In ICLP, Volume 5366 of Lecture Notes in Computer Science, pp. 407–424. Springer.Google Scholar
Calimeri, F., Fuscà, D., Perri, S., and Zangari, J. 2017. I-DLV: the new intelligent grounder of DLV. Intelligenza Artificiale 11, 1, 5–20.CrossRefGoogle Scholar
Calimeri, F., Germano, S., Ianni, G., Pacenza, F., Perri, S., and Zangari, J. 2018. Integrating rule-based AI tools into mainstream game development. In RuleML+RR 2018, pp. 310–317.Google Scholar
Calimeri, F., Ianni, G., Perri, S., and Zangari, J. 2013. The eternal battle between determinism and nondeterminism: preliminary studies in the sudoku domain. 20th RCRA International Workshop. 2013.Google Scholar
Calimeri, F., Perri, S., and Zangari, J. 2019. Optimizing answer set computation via heuristic-based decomposition. Theory and Practice of Logic Programming, 1–26.Google Scholar
Dal Palù, A., Dovier, A., Pontelli, E., and Rossi, G. 2009. GASP: answer set programming with lazy grounding. Fundam. Inform. 96, 3, 297–322.Google Scholar
Dantsin, E., Eiter, T., Gottlob, G., and Voronkov, A. 2001. Complexity and expressive power of logic programming. ACM Comput. Surv. 33, 3, 374–425.CrossRefGoogle Scholar
Dao-Tran, M., Eiter, T., Fink, M., Weidinger, G., and Weinzierl, A. 2012. Omiga : An open minded grounding on-the-fly answer set solver. In JELIA, Volume 7519 of LNCS, pp. 480–483. Springer.Google Scholar
De Cat, B., Denecker, M., and Stuckey, P. 2012. Lazy model expansion by incremental grounding. In Technical Communications of ICLP 2012, pp. 201–211.Google Scholar
Eiter, T., Ianni, G., and Krennwallner, T. 2009. Answer set programming: A primer. In Reasoning Web School 2009, Volume 5689 of LNCS, pp. 40–110. Springer.Google Scholar
Faber, W., Leone, N., and Pfeifer, G. 2004. Recursive aggregates in disjunctive logic programs: Semantics and complexity. In JELIA, Volume 3229 of LNCS, pp. 200–212. Springer.Google Scholar
Fages, F. 1994. Consistency of clark’s completion and existence of stable models. Meth. of Logic in CS 1, 1, 51–60.Google Scholar
Gebser, M., Kaminski, R., Kaufmann, B., Romero, J., and Schaub, T. 2015. Progress in clasp series 3. In LPNMR 2015, Volume 9345 of LNCS, pp. 368–383. Springer.Google Scholar
Gebser, M., Kaminski, R., Kaufmann, B., and Schaub, T. 2019. Multi-shot ASP solving with clingo. TPLP 19, 1, 27–82.Google Scholar
Gebser, M., Kaminski, R., König, A., and Schaub, T. 2011. Advances in gringo series 3. In LPNMR, Volume 6645 of LNCS, pp. 345–351. Springer.Google Scholar
Gebser, M., Maratea, M., and Ricca, F. 2017. The sixth answer set programming competition. J. Artif. Intell. Res. 60, 41–95.Google Scholar
Gelfond, M. and Lifschitz, V. 1991. Classical negation in logic programs and disjunctive databases. New Generation Computing 9, 3/4, 365–386.CrossRefGoogle Scholar
Herbrand, J. 1930. Recherches sur la théorie de la démonstration.Google Scholar
Kaufmann, B., Leone, N., Perri, S., and Schaub, T. 2016. Grounding and solving in answer set programming. AI Magazine 37, 3, 25–32.CrossRefGoogle Scholar
Lefèvre, C., Béatrix, C., Stéphan, I., and Garcia, L. 2017. Asperix, a first-order forward chaining approach for answer set computing. TPLP 17, 3, 266–310.Google Scholar
Leone, N., Rullo, P., and Scarcello, F. 1997. Disjunctive stable models: Unfounded sets, fixpoint semantics, and computation. Information and Computation 135, 2, 69–112.CrossRefGoogle Scholar
Motik, B., Nenov, Y., Piro, R., and Horrocks, I. 2019. Maintenance of datalog materialisations revisited. Artificial Intelligence 269, 76–136.Google Scholar
Nethercote, N., Stuckey, P., Becket, R., Brand, S., Duck, G., and Tack, G. 2007. Minizinc: Towards a standard CP modelling language. In CP 2007, pp. 529–543.Google Scholar
Pérez-Liébana, D., Samothrakis, S., Togelius, J., Schaul, T., and Lucas, S. 2016. General video game AI: competition, challenges and opportunities. In AAAI 2016, pp. 4335–4337.Google Scholar
Tarski, A. 1955. A lattice-theoretical fixpoint theorem and its applications. Pacific J. Math. 5, 2, 285–309.CrossRefGoogle Scholar
Ullman, J. 1988. Principles of Database and Knowledge-Base Systems, Volume I, Volume 14 of Principles of computer science series. Computer Science Press.Google Scholar
Van Gelder, A., Ross, K., and Schlipf, J. 1991. The Well-Founded Semantics for General Logic Programs. Journal of the ACM 38, 3, 620–650.Google Scholar
Weinzierl, A. 2017. Blending lazy-grounding and CDNL search for answer-set solving. In LPNMR, Volume 10377 of LNCS, pp. 191–204. Springer.Google Scholar
Supplementary material: PDF

Calimeri et al. supplementary material

Calimeri et al. supplementary material

Download Calimeri et al. supplementary material(PDF)
PDF 118.7 KB